The sentences obtained for relation extraction using distant supervision are generally long, and two entities are often located far from each other in these sentences. [1]}, [2]} used convolutional neural networks (CNN) with max-pooling to find the relation between two given entities in distantly supervised data. Though these models have performed reasonably well on distantly supervised data, they sometimes fail to predict the correct relation when sentences are long, and entities are far from each other. CNN models with max-pooling have limitations in understanding the semantic similarity of words with the given entities, and they also fail to capture the long-distance dependencies among the words and entities such as co-reference. Besides, all the words in a sentence may not be equally important in finding the relation, and this issue is more prominent in long sentences. Prior CNN-based models have limitations in identifying the multiple relevant factors to focus on in sentence-level relation extraction.
